Adaptive Speech Compression Based on Discrete Wave Atoms Transform

Bousselmi, Souha; Nouredine, Aloui; Chérif, Adnane

doi:10.11591/ijece.v6i5.10826

Cited by 2 publications

(1 citation statement)

References 17 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wavelet transform is an effective tool in analyzing the transient current and signal associated with faults, both in frequency and in the time-domain [12], [13] and is ideal for dealing with non-stationary signal. This can improve the accuracy, reliability of the detection and classification of power quality disturbance [14], and features can be applied to determine the fault location estimation [15].…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Fault Location and Classification on Parallel Transmission Line using Wavelet based on Clarke’s Transformation

Saini

Zin

Mustafa

et al. 2018

IJECE

View full text Add to dashboard Cite

This paper proposed a new algorithm for fault location and classification using wavelet based on Clarke's transformation to obtain the fault current. This novel method of fault current approach is studied by comparing the use of the glide path of the fault voltage. The current alpha and beta (Current Mode) were used to transform the signal using discrete wavelet transform (DWT). The fault location was determined by using the Clarke's transformation, and then turned into a wavelet, which was very precise and thorough. The most accurate was the mother wavelet Db4 which had the fastest time and smallest error detection when compared with the other wavelet mothers. In this study, the Clarke's transformation is also compared with the Karenbauer's, which has produced results with similar error percentage. The simulation results using PSCAD / EMTDC software showed that the proposed algorithm could distinguish internal and external faults to get the current signal in the transformation of a signal fault.

show abstract

Section: Introductionmentioning

confidence: 99%

Algorithm for Fault Location and Classification on Parallel Transmission Line using Wavelet based on Clarke’s Transformation

Saini

Zin

Mustafa

et al. 2018

IJECE

View full text Add to dashboard Cite

show abstract

A psychoacoustic model and a Filter Bank Design using optimization for speech compression

Talbi¹,

Bouhlel²

2018

AMA_B

View full text Add to dashboard Cite

In this paper we propose a new speech compression technique employing psychoacoustic model and a general approach for Filter Bank Design using optimization. This technique is inspired from an audio compression technique using psychoacoustic model and a Modified Discrete Cosine Transform (MDCT) filter banks of 32 filters. In fact, in this proposed approach, we have used Uniform/Non-Uniform Filter Bank (which is designed using optimization) instead of a MDCT filter banks of 32 filters. The two techniques are evaluated and compared with each other by computing bits before and after compression. They are tested and applied to different speech signals. The simulation results obtained from the computation of the compressed files size and the Compression Ratios (CR), show that the proposed technique outperforms the second one. In term of perceptual speech quality, the outputs speech signals of the proposed compression system are with good quality. This is justified by the computation of SNR (Signal to Noise Ratio), PSNR (Peak Signal to Noise Ratio), NRMSE (Normalized Root Mean Square Error) and PESQ (Perceptual evaluation of speech quality). We have also compared the proposed technique to one previous research work which is a speech compression technique based on Discrete Wavelet Transform (DWT) and integrating a Voice Activity Detection (VAD) Module. This comparison is also based on the computation of SNR, PSNR, NRMSE, PESQ and CR and the obtained results show that the proposed technique outperforms this third technique based on DWT and VAD.

show abstract

Adaptive Speech Compression Based on Discrete Wave Atoms Transform

Cited by 2 publications

References 17 publications

Algorithm for Fault Location and Classification on Parallel Transmission Line using Wavelet based on Clarke’s Transformation

Algorithm for Fault Location and Classification on Parallel Transmission Line using Wavelet based on Clarke’s Transformation

A psychoacoustic model and a Filter Bank Design using optimization for speech compression

Contact Info

Product

Resources

About